Recently, deep learning-based methods have been widely used in abdominal organs segmentation tasks, especially the UNet-based deep networks. The main challenge in this task lies in complex anatomical structures, the unclear boundary of soft tissues, high resolution of images, and extremely unbalanced sizes among large and small organs, etc. Many works have also been attempted to handle these challenges. E. Gibson et al. [1]} proposed a dense V-network to segment 8 organs from CT volumes, which enables high-resolution activation maps through memory-efficient dropout and feature reuse. Y. Wang et al. [2]} presented a novel framework for abdominal multi-organ segmentation by using organ-attention networks with reverse connections and evaluated it on an in-house dataset. X. Liang et al. [3]} combined the inter-and intra-patient deformation data augmentation with multi-scale Attention-UNet [4]} for accurate abdominal multi-organ segmentation. Y. Tang et al. [5]} proposed a batch-based method plus random shifting strategy to boost the performance of multi-organ segmentation from high-resolution abdomen CT volumes. More recently, transformer-based methods [6]}, [7]} are also used to explicitly model the long-range dependence to capture the relationship of multi-organ for accurate segmentation. Although the above methods have achieved promising results, they are also limited by requiring large scale carefully annotated dataset. To reduce label costs, Y. Zhou et al. [8]} proposed a co-training-based semi-supervised method for abdominal multi-organ segmentation, which reduces almost half of annotation costs. Furthermore, Y. Zhou et al. [9]} proposed a prior-aware neural network that incorporates anatomical priors on abdominal organ sizes to train models from several partially-labeled datasets. In this work, we investigate a more annotation-efficient method for abdominal multi-organ segmentation just with very sparse annotations (scribbles).
<TABLE><TABLE><FIGURE><TABLE><TABLE>